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ds2 <- readRDS("ds2.rds")

umapplot

ds0 <- readRDS("ds0.rds")

周细胞的存在

library(igraph)

基因表达

ds2 -> ds1 无监督聚类

ds2data <- get_data_table(ds2FbM,type = "data")
ds1data <- get_data_table(ds1FbM,type = "data")

# ds2expr <- data.frame(expr = ds2data["DCN",], sample = "ds2", gene = "DCN")
# rownames(ds2expr) <- NULL
# ds1expr <- data.frame(expr = ds1data["DCN",], sample = "ds1")
# rownames(ds1expr) <- NULL
# merge_expr <- rbind(ds2expr,ds1expr)

genes_to_show <- c("DCN","LUM","MMP2","ACTA2","TNFRSF11B","FBLN1")

func <- function(gene, sample, datable){
  data.frame(expr = datable[gene,], sample = sample, gene = gene)
}

merge_expr <- data.frame()

for (i in lapply(genes_to_show, func,"ds1",ds1data))
{
  merge_expr <- rbind(merge_expr,i)
}
for (i in lapply(genes_to_show, func,"ds2",ds2data))
{
  merge_expr <- rbind(merge_expr,i)
}


rownames(merge_expr) <- NULL



Data_summary <- Rmisc::summarySE(merge_expr, measurevar="expr", groupvars=c("sample","gene"))
head(Data_summary)


ggobj <- ggplot(merge_expr,aes(x = gene, y = expr,fill = sample)) +
  geom_split_violin(trim= F, color="white", scale = "area") + 
  geom_point(data = Data_summary,aes(x = gene, y= expr), pch=19,
             position=position_dodge(0.2),size= 1) + #绘制均值位置
  geom_errorbar(data = Data_summary, aes(ymin = expr-ci, ymax= expr+ci), 
                width= 0.05, 
                position= position_dodge(0.2), #误差线位置，和均值位置相匹配
                color="black",
                alpha = 0.7,
                size= 0.5) +
  scale_fill_manual(values = c("#b1d6fb", "#fd9999"))+ 
  labs(y=("Log2 expression"),x=NULL,title = "Split violin") + 
  theme_classic()+ mytheme + stat_compare_means(aes(group = sample),
                     label = "p.format",
                     method = "wilcox.test",
                     label.y = max(merge_expr$expr),
                      hide.ns = F)
ggsave("unds2tods1.svg", device = svg, plot = ggobj, height = 6, width = 10)

监督聚类结果

这里使用numeric的分群

ds2FbM <- subset(ds2,ident = "1")
ds1FbM <- subset(ds1,ident = "1")

ds2data <- get_data_table(ds2FbM,type = "data")
ds1data <- get_data_table(ds1FbM,type = "data")

# ds2expr <- data.frame(expr = ds2data["DCN",], sample = "ds2", gene = "DCN")
# rownames(ds2expr) <- NULL
# ds1expr <- data.frame(expr = ds1data["DCN",], sample = "ds1")
# rownames(ds1expr) <- NULL
# merge_expr <- rbind(ds2expr,ds1expr)

genes_to_show <- c("DCN","LUM","MMP2","ACTA2","TNFRSF11B","FBLN1")

func1 <- function(gene, sample, datable){
  data.frame(expr = datable[gene,], sample = sample, gene = gene)
}

merge_expr <- data.frame()

for (i in lapply(genes_to_show, func1,"ds1",ds1data))
{
  merge_expr <- rbind(merge_expr,i)
}
for (i in lapply(genes_to_show, func1,"ds2",ds2data))
{
  merge_expr <- rbind(merge_expr,i)
}


rownames(merge_expr) <- NULL

Data_summary <- Rmisc::summarySE(merge_expr, measurevar="expr", groupvars=c("sample","gene"))
head(Data_summary)

ggobj <- ggplot(merge_expr,aes(x = gene, y = expr,fill = sample)) +
  geom_split_violin(trim= F, color="white", scale = "area") + 
  geom_point(data = Data_summary,aes(x = gene, y= expr), pch=19,
             position=position_dodge(0.2),size= 1) + #绘制均值位置
  geom_errorbar(data = Data_summary, aes(ymin = expr-ci, ymax= expr+ci), 
                width= 0.05, 
                position= position_dodge(0.2), #误差线位置，和均值位置相匹配
                color="black",
                alpha = 0.7,
                size= 0.5) +
  scale_fill_manual(values = c("#b1d6fb", "#fd9999"))+ 
  labs(y=("Log2 expression"),x=NULL,title = "Split violin") + 
  theme_classic()+ mytheme + stat_compare_means(aes(group = sample),
                     label = "p.format",
                     method = "wilcox.test",
                     label.y = max(merge_expr$expr),
                      hide.ns = F)

ggsave("supds2tods1.svg", device = svg, plot = ggobj, height = 6, width = 10)

ds2 -> ds0

无监督聚类

ds2data <- get_data_table(ds2FbM,type = "data")
ds0data <- get_data_table(ds0FbM,type = "data")

genes_to_show <- c("DCN","LUM","MMP2","ACTA2","TNFRSF11B","FBLN1")

func <- function(gene, sample, datable){
  data.frame(expr = datable[gene,], sample = sample, gene = gene)
}

merge_expr <- data.frame()

for (i in lapply(genes_to_show, func,"ds0",ds0data))
{
  merge_expr <- rbind(merge_expr,i)
}
for (i in lapply(genes_to_show, func,"ds2",ds2data))
{
  merge_expr <- rbind(merge_expr,i)
}

rownames(merge_expr) <- NULL

Data_summary <- Rmisc::summarySE(merge_expr, measurevar="expr", groupvars=c("sample","gene"))
head(Data_summary)


ggobj <- ggplot(merge_expr,aes(x = gene, y = expr,fill = sample)) +
  geom_split_violin(trim= F, color="white", scale = "area") + 
  geom_point(data = Data_summary,aes(x = gene, y= expr), pch=19,
             position=position_dodge(0.2),size= 1) + #绘制均值位置
  geom_errorbar(data = Data_summary, aes(ymin = expr-ci, ymax= expr+ci), 
                width= 0.05, 
                position= position_dodge(0.2), #误差线位置，和均值位置相匹配
                color="black",
                alpha = 0.7,
                size= 0.5) +
  scale_fill_manual(values = c("#b1d6fb", "#fd9999"))+ 
  labs(y=("Log2 expression"),x=NULL,title = "Split violin") + 
  theme_classic()+ mytheme + stat_compare_means(aes(group = sample),
                     label = "p.format",
                     method = "wilcox.test",
                     label.y = max(merge_expr$expr),
                      hide.ns = F)

监督聚类

ds2data <- get_data_table(ds2FbM,type = "data")
ds0data <- get_data_table(ds0FbM,type = "data")

genes_to_show <- c("DCN","LUM","MMP2","ACTA2","TNFRSF11B","FBLN1")

func <- function(gene, sample, datable){
  data.frame(expr = datable[gene,], sample = sample, gene = gene)
}

merge_expr <- data.frame()

for (i in lapply(genes_to_show, func,"ds0",ds0data))
{
  merge_expr <- rbind(merge_expr,i)
}
for (i in lapply(genes_to_show, func,"ds2",ds2data))
{
  merge_expr <- rbind(merge_expr,i)
}

rownames(merge_expr) <- NULL

Data_summary <- Rmisc::summarySE(merge_expr, measurevar="expr", groupvars=c("sample","gene"))
head(Data_summary)


ggobj <- ggplot(merge_expr,aes(x = gene, y = expr,fill = sample)) +
  geom_split_violin(trim= F, color="white", scale = "area") + 
  geom_point(data = Data_summary,aes(x = gene, y= expr), pch=19,
             position=position_dodge(0.2),size= 1) + #绘制均值位置
  geom_errorbar(data = Data_summary, aes(ymin = expr-ci, ymax= expr+ci), 
                width= 0.05, 
                position= position_dodge(0.2), #误差线位置，和均值位置相匹配
                color="black",
                alpha = 0.7,
                size= 0.5) +
  scale_fill_manual(values = c("#b1d6fb", "#fd9999"))+ 
  labs(y=("Log2 expression"),x=NULL,title = "Split violin") + 
  theme_classic()+ mytheme + stat_compare_means(aes(group = sample),
                     label = "p.format",
                     method = "wilcox.test",
                     label.y = max(merge_expr$expr),
                      hide.ns = F)

ggsave("supds2tods0.svg", device = svg, plot = ggobj, height = 6, width = 10)

ds0 -> ds2

##无监督聚类

ds2data <- get_data_table(ds2FbM,type = "data")
ds0data <- get_data_table(ds0FbM,type = "data")

# genes_to_show <- c("IGFBP2","MGP","MYH11","DCN","TNFRSF11B")
genes_to_show <- c("DCN","LUM","MMP2","ACTA2","TNFRSF11B","FBLN1")

func <- function(gene, sample, datable){
  data.frame(expr = datable[gene,], sample = sample, gene = gene)
}

merge_expr <- data.frame()

for (i in lapply(genes_to_show, func,"ds0",ds0data))
{
  merge_expr <- rbind(merge_expr,i)
}
for (i in lapply(genes_to_show, func,"ds2",ds2data))
{
  merge_expr <- rbind(merge_expr,i)
}

rownames(merge_expr) <- NULL

Data_summary <- Rmisc::summarySE(merge_expr, measurevar="expr", groupvars=c("sample","gene"))
head(Data_summary)


ggobj <- ggplot(merge_expr,aes(x = gene, y = expr,fill = sample)) +
  geom_split_violin(trim= F, color="white", scale = "area") + 
  geom_point(data = Data_summary,aes(x = gene, y= expr), pch=19,
             position=position_dodge(0.2),size= 1) + #绘制均值位置
  geom_errorbar(data = Data_summary, aes(ymin = expr-ci, ymax= expr+ci), 
                width= 0.05, 
                position= position_dodge(0.2), #误差线位置，和均值位置相匹配
                color="black",
                alpha = 0.7,
                size= 0.5) +
  scale_fill_manual(values = c("#b1d6fb", "#fd9999"))+ 
  labs(y=("Log2 expression"),x=NULL,title = "Split violin") + 
  theme_classic()+ mytheme + stat_compare_means(aes(group = sample),
                     label = "p.format",
                     method = "wilcox.test",
                     label.y = max(merge_expr$expr),
                      hide.ns = F)

ggsave("2_unds0tods2.svg", device = svg, plot = ggobj, height = 6, width = 10)

监督聚类

ds0 -> ds2

func <- function(gene, sample, datable){
  data.frame(expr = datable[gene,], sample = sample, gene = gene)
}

merge_expr <- data.frame()

for (i in lapply(genes_to_show, func,"ds0",ds0data))
{
  merge_expr <- rbind(merge_expr,i)
}
for (i in lapply(genes_to_show, func,"ds2",ds2data))
{
  merge_expr <- rbind(merge_expr,i)
}

rownames(merge_expr) <- NULL

Data_summary <- Rmisc::summarySE(merge_expr, measurevar="expr", groupvars=c("sample","gene"))
head(Data_summary)


ggobj <- ggplot(merge_expr,aes(x = gene, y = expr,fill = sample)) +
  geom_split_violin(trim= F, color="white", scale = "area") + 
  geom_point(data = Data_summary,aes(x = gene, y= expr), pch=19,
             position=position_dodge(0.2),size= 1) + #绘制均值位置
  geom_errorbar(data = Data_summary, aes(ymin = expr-ci, ymax= expr+ci), 
                width= 0.05, 
                position= position_dodge(0.2), #误差线位置，和均值位置相匹配
                color="black",
                alpha = 0.7,
                size= 0.5) +
  scale_fill_manual(values = c("#b1d6fb", "#fd9999"))+ 
  labs(y=("Log2 expression"),x=NULL,title = "Split violin") + 
  theme_classic()+ mytheme + stat_compare_means(aes(group = sample),
                     label = "p.format",
                     method = "wilcox.test",
                     label.y = max(merge_expr$expr),
                      hide.ns = F)
ggobj
ggsave("2_supds0tods2.svg", device = svg, plot = ggobj, height = 6, width = 10)

ds0 -> ds1

##无监督聚类

genes_to_show <- c("DCN","LUM","MMP2","ACTA2","TNFRSF11B","FBLN1")

func <- function(gene, sample, datable){
  data.frame(expr = datable[gene,], sample = sample, gene = gene)
}

merge_expr <- data.frame()

for (i in lapply(genes_to_show, func,"ds0",ds0data))
{
  merge_expr <- rbind(merge_expr,i)
}
for (i in lapply(genes_to_show, func,"ds1",ds1data))
{
  merge_expr <- rbind(merge_expr,i)
}

rownames(merge_expr) <- NULL

Data_summary <- Rmisc::summarySE(merge_expr, measurevar="expr", groupvars=c("sample","gene"))
head(Data_summary)


ggobj <- ggplot(merge_expr,aes(x = gene, y = expr,fill = sample)) +
  geom_split_violin(trim= F, color="white", scale = "area") + 
  geom_point(data = Data_summary,aes(x = gene, y= expr), pch=19,
             position=position_dodge(0.2),size= 1) + #绘制均值位置
  geom_errorbar(data = Data_summary, aes(ymin = expr-ci, ymax= expr+ci), 
                width= 0.05, 
                position= position_dodge(0.2), #误差线位置，和均值位置相匹配
                color="black",
                alpha = 0.7,
                size= 0.5) +
  scale_fill_manual(values = c("#b1d6fb", "#fd9999"))+ 
  labs(y=("Log2 expression"),x=NULL,title = "Split violin") + 
  theme_classic()+ mytheme + stat_compare_means(aes(group = sample),
                     label = "p.format",
                     method = "wilcox.test",
                     label.y = max(merge_expr$expr),
                      hide.ns = F)
ggobj
ggsave("2_unds0tods1.svg", device = svg, plot = ggobj, height = 6, width = 10)

监督聚类

ds0 -> ds1

merge_expr <- data.frame()

for (i in lapply(genes_to_show, func1,"ds0",ds0data))
{
  merge_expr <- rbind(merge_expr,i)
}
for (i in lapply(genes_to_show, func1,"ds1",ds1data))
{
  merge_expr <- rbind(merge_expr,i)
}

rownames(merge_expr) <- NULL

Data_summary <- Rmisc::summarySE(merge_expr, measurevar="expr", groupvars=c("sample","gene"))
head(Data_summary)


ggobj <- ggplot(merge_expr,aes(x = gene, y = expr,fill = sample)) +
  geom_split_violin(trim= F, color="white", scale = "area") + 
  geom_point(data = Data_summary,aes(x = gene, y= expr), pch=19,
             position=position_dodge(0.2),size= 1) + #绘制均值位置
  geom_errorbar(data = Data_summary, aes(ymin = expr-ci, ymax= expr+ci), 
                width= 0.05, 
                position= position_dodge(0.2), #误差线位置，和均值位置相匹配
                color="black",
                alpha = 0.7,
                size= 0.5) +
  scale_fill_manual(values = c("#b1d6fb", "#fd9999"))+ 
  labs(y=("Log2 expression"),x=NULL,title = "Split violin") + 
  theme_classic()+ mytheme + stat_compare_means(aes(group = sample),
                     label = "p.format",
                     method = "wilcox.test",
                     label.y = max(merge_expr$expr),
                      hide.ns = F)
ggobj

mytheme <- theme(plot.title = element_text(size = 12,color="black",hjust = 0.5),
                     axis.title = element_text(size = 12,color ="black"), 
                     axis.text = element_text(size= 12,color = "black"),
                     panel.grid.minor.y = element_blank(),
                     panel.grid.minor.x = element_blank(),
                     axis.text.x = element_text(angle = 45, hjust = 1 ),
                     panel.grid=element_blank(),
                     legend.position = "top",
                     legend.text = element_text(size= 12),
                     legend.title= element_text(size= 12)) 

# https://stackoverflow.com/a/45614547
GeomSplitViolin <- ggproto("GeomSplitViolin", GeomViolin, draw_group = function(self, data, ..., draw_quantiles = NULL){
  data <- transform(data, xminv = x - violinwidth * (x - xmin), xmaxv = x + violinwidth * (xmax - x))
  grp <- data[1,'group']
  newdata <- plyr::arrange(transform(data, x = if(grp%%2==1) xminv else xmaxv), if(grp%%2==1) y else -y)
  newdata <- rbind(newdata[1, ], newdata, newdata[nrow(newdata), ], newdata[1, ])
  newdata[c(1,nrow(newdata)-1,nrow(newdata)), 'x'] <- round(newdata[1, 'x']) 
  if (length(draw_quantiles) > 0 & !scales::zero_range(range(data$y))) {
    stopifnot(all(draw_quantiles >= 0), all(draw_quantiles <= 
                                              1))
    quantiles <- ggplot2:::create_quantile_segment_frame(data, draw_quantiles)
    aesthetics <- data[rep(1, nrow(quantiles)), setdiff(names(data), c("x", "y")), drop = FALSE]
    aesthetics$alpha <- rep(1, nrow(quantiles))
    both <- cbind(quantiles, aesthetics)
    quantile_grob <- GeomPath$draw_panel(both, ...)
    ggplot2:::ggname("geom_split_violin", grid::grobTree(GeomPolygon$draw_panel(newdata, ...), quantile_grob))
  }
  else {
    ggplot2:::ggname("geom_split_violin", GeomPolygon$draw_panel(newdata, ...))
  }
})

geom_split_violin <- function (mapping = NULL, data = NULL, stat = "ydensity", position = "identity", ..., draw_quantiles = NULL, trim = TRUE, scale = "area", na.rm = FALSE, show.legend = NA, inherit.aes = TRUE) {
  layer(data = data, mapping = mapping, stat = stat, geom = GeomSplitViolin, position = position, show.legend = show.legend, inherit.aes = inherit.aes, params = list(trim = trim, scale = scale, draw_quantiles = draw_quantiles, na.rm = na.rm, ...))
}

scatter 比较 fig.3 DCN 和 LUM 在 FbM 中 sup vs unsup

ds2 -> ds0

bst_model <- readRDS("ds2_model.rds")
ds2_data <- get_data_table(ds2, highvar = F, type = "data")
Idents(ds2) <- ds2$seurat_clusters
Idents(ds0) <- ds0$seurat_clusters
temp <- get_data_table(ds0, highvar = F, type = "data")
ds0_data <- matrix(data=0, nrow = length(rownames(ds2_data)), ncol = length(colnames(temp)), 
                   byrow = FALSE, dimnames = list(rownames(ds2_data),colnames(temp)))
for(i in intersect(rownames(ds2_data), rownames(temp))){
  ds0_data[i,] <- temp[i,]
}
rm(temp)
ds0_label <- as.numeric(as.character(Idents(ds0)))
colnames(ds0_data) <- NULL
ds0_test_data <- list(data = t(as(ds0_data,"dgCMatrix")), label = ds0_label)
ds0_test <- xgb.DMatrix(data = ds0_test_data$data,label = ds0_test_data$label)

#预测结果
predict_ds0_test <- predict(bst_model, newdata = ds0_test)
predict_prop_ds0 <- matrix(data=predict_ds0_test, nrow = length(levels(Idents(ds2))), 
                           ncol = ncol(ds0), byrow = FALSE, 
                           dimnames = list(levels(Idents(ds2)),colnames(ds0)))

## 得到分群结果
ds0_res <- apply(predict_prop_ds0,2,func,rownames(predict_prop_ds0))
Idents(ds0) <- factor(ds0_res,levels = c(0:4))
umapplot(ds0)
ds0$supclustering <- Idents(ds0) #保存监督聚类结果

Idents(ds0) <- ds0$supclustering
sup_ds0FBM <- subset(ds0, ident = "1")
Idents(ds0) <- ds0$Classification1
unsup_ds0FBM <- subset(ds0, ident = "Fibromyocyte")
ref_unsup_ds2FBM <- subset(ds2, ident = "1")

data1 <- FetchData(object = sup_ds0FBM, vars = c("LUM", "ACTA2","BGN","TAGLN"))
rownames(data1) <-  NULL
data1$group <- "sup"

data2 <- FetchData(object = unsup_ds0FBM, vars = c("LUM", "ACTA2","BGN","TAGLN"))
rownames(data2) <-  NULL
data2$group <- "unsup"

data3 <- FetchData(object = ref_unsup_ds2FBM, vars = c("LUM", "ACTA2","BGN","TAGLN"))
rownames(data3) <-  NULL
data3$group <- "ref"

data <- rbind(data1,data2,data3)

ggplot(data, aes(x=LUM, y=BGN, color = group, group = group)) +
  geom_point(size = 3,alpha = 0.1) + 
  geom_smooth(method=lm , se=TRUE) +
  theme_classic() +  theme(axis.title = element_text(size = 20,color = "black"),
        axis.text = element_text(size = 20,color = "black"),
        axis.line = element_line(size = 1),
        axis.ticks = element_line(size = 1),
        title = element_text(size = 20))

`geom_smooth()` using formula 'y ~ x'

ggplot(data, aes(x=LUM, y=ACTA2, color = group, group = group)) +
  geom_point(size = 3,alpha = 0.1) + 
  geom_smooth(method=lm , se=TRUE) +
  theme_classic() +  theme(axis.title = element_text(size = 20,color = "black"),
        axis.text = element_text(size = 20,color = "black"),
        axis.line = element_line(size = 1),
        axis.ticks = element_line(size = 1),
        title = element_text(size = 20))

`geom_smooth()` using formula 'y ~ x'

ggplot(data, aes(x=TAGLN, y=ACTA2, color = group, group = group)) +
  geom_point(size = 3,alpha = 0.1) + 
  geom_smooth(method=lm , se=TRUE) +
  theme_classic() +  theme(axis.title = element_text(size = 20,color = "black"),
        axis.text = element_text(size = 20,color = "black"),
        axis.line = element_line(size = 1),
        axis.ticks = element_line(size = 1),
        title = element_text(size = 20))

`geom_smooth()` using formula 'y ~ x'

scatter 比较 fig.3 DCN 和 LUM 在 FbM 中 sup vs unsup

ds2 -> ds1

Idents(ds1) <- ds1$supclustering
sup_ds1FBM <- subset(ds1, ident = "1")
Idents(ds1) <- ds1$Classification1
unsup_ds1FBM <- subset(ds1, ident = "Fibromyocyte")
ref_unsup_ds2FBM <- subset(ds2, ident = "1")

data1 <- FetchData(object = sup_ds1FBM, vars = c("LUM", "ACTA2","BGN","TAGLN"))
rownames(data1) <-  NULL
data1$group <- "sup"

data2 <- FetchData(object = unsup_ds1FBM, vars = c("LUM", "ACTA2","BGN","TAGLN"))
rownames(data2) <-  NULL
data2$group <- "unsup"

data3 <- FetchData(object = ref_unsup_ds2FBM, vars = c("LUM", "ACTA2","BGN","TAGLN"))
rownames(data3) <-  NULL
data3$group <- "ref"

data <- rbind(data1,data2,data3)

ggplot(data, aes(x=LUM, y=BGN, color = group, group = group)) +
  geom_point(size = 3,alpha = 0.1) + 
  geom_smooth(method=lm , se=TRUE) +
  theme_classic() +  theme(axis.title = element_text(size = 20,color = "black"),
        axis.text = element_text(size = 20,color = "black"),
        axis.line = element_line(size = 1),
        axis.ticks = element_line(size = 1),
        title = element_text(size = 20))

`geom_smooth()` using formula 'y ~ x'

ggplot(data, aes(x=LUM, y=ACTA2, color = group, group = group)) +
  geom_point(size = 3,alpha = 0.1) + 
  geom_smooth(method=lm , se=TRUE) +
  theme_classic() +  theme(axis.title = element_text(size = 20,color = "black"),
        axis.text = element_text(size = 20,color = "black"),
        axis.line = element_line(size = 1),
        axis.ticks = element_line(size = 1),
        title = element_text(size = 20))

`geom_smooth()` using formula 'y ~ x'

ggplot(data, aes(x=TAGLN, y=ACTA2, color = group, group = group)) +
  geom_point(size = 3,alpha = 0.1) + 
  geom_smooth(method=lm , se=TRUE) +
  theme_classic() +  theme(axis.title = element_text(size = 20,color = "black"),
        axis.text = element_text(size = 20,color = "black"),
        axis.line = element_line(size = 1),
        axis.ticks = element_line(size = 1),
        title = element_text(size = 20))

`geom_smooth()` using formula 'y ~ x'

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